Evidence map›Paper›PMID 38076815›Full record

ArticlebioRxiv : the preprint server for biology2025

ConvexML: Fast and accurate branch length estimation under irreversible mutation models, illustrated through applications to CRISPR/Cas9-based lineage tracing.

Sebastian Prillo, Akshay Ravoor, Nir Yosef, Yun S Song

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Sebastian PrilloComputer Science Division, University of California, Berkeley, USA.
Akshay RavoorComputer Science Division, University of California, Berkeley, USA.
Nir YosefComputer Science Division, University of California, Berkeley, USA.
Yun S SongComputer Science Division, University of California, Berkeley, USA.ORCID 0000-0002-0734-9868

Funding

Scalable Computational Methods for Genealogical Inference: from species level to single cellsR01HG013117 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI Ian H Holmes, RASMUS NIELSEN · 2024 to 2026
$1.7M
Scalable Computational Methods for Genealogical Inference: from species level to single cellsR56HG013117 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI HOLMES, IAN H, NIELSEN, RASMUS · 2023 to 2023
$315k
NHGRI NIH HHS R01 HG013117NHGRI NIH HHS R56 HG013117
6 · The paper itself

Abstract

Branch length estimation is a fundamental problem in Statistical Phylogenetics and a core component of tree reconstruction algorithms. Traditionally, general time-reversible mutation models are employed, and many software tools exist for this scenario. With the advent of CRISPR/Cas9 lineage tracing technologies, there has been significant interest in the study of branch length estimation under

Indexed as

branch length estimationconvex optimizationCRISPR/Cas9irreversible mutation modelsMaximum likelihood estimationparsimonypseudocountssingle-cell lineage tracing

Identifiers

PMID38076815
PMCPMC10705529

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.